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Significant advances are being made in speech emotion recognition (SER) usingdeep learning models. Nonetheless, training SER systems remains challenging,requiring both time and costly resources. Like many other machine learningtasks, acquiring datasets for SER requires substantial data annotation efforts,including transcription and labeling. These annotation processes presentchallenges when attempting to scale up conventional SER systems. Recentdevelopments in foundational models have had a tremendous impact, giving riseto applications such as ChatGPT. These models have enhanced human-computerinteractions including bringing unique possibilities for streamlining datacollection in fields like SER. In this research, we explore the use offoundational models to assist in automating SER from transcription andannotation to augmentation. Our study demonstrates that these models cangenerate transcriptions to enhance the performance of SER systems that relysolely on speech data. Furthermore, we note that annotating emotions fromtranscribed speech remains a challenging task. However, combining outputs frommultiple LLMs enhances the quality of annotations. Lastly, our findings suggestthe feasibility of augmenting existing speech emotion datasets by annotatingunlabeled speech samples.

Foundation Model Assisted Automatic Speech Emotion Recognition: Transcribing, Annotating, and Augmenting